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Gupta M, Sharma A, Sharma DK, Nirola M, Dhungel P, Patel A, Singh H, Gupta A. Tracing the COVID-19 spread pattern in India through a GIS-based spatio-temporal analysis of interconnected clusters. Sci Rep 2024; 14:847. [PMID: 38191902 PMCID: PMC10774287 DOI: 10.1038/s41598-023-50933-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024] Open
Abstract
Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation's data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran's I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran's I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with [Formula: see text] of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.
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Affiliation(s)
- Mousumi Gupta
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India.
| | - Arpan Sharma
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Dhruva Kumar Sharma
- Department of Pharmacology, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Tadong Campus, Gangtok, 737102, India
| | - Madhab Nirola
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Prasanna Dhungel
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India
| | - Ashok Patel
- Kusuma School of Biological Sciences, Indian Institute of Technology, Delhi, 110016, India
| | - Harpreet Singh
- Division of Biomedical Informatics, Indian Council of Medical Research, Delhi, 110029, India
| | - Amlan Gupta
- Department of Transfusion Medicine, Jay Prabha Medanta Super Speciality Hospital, Patna, 800020, India
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